Optimization of moth-eye antireflection schemes for silicon solar cells

Nanostructured moth-eye antireflection schemes for silicon solar cells are simulated using rigorous coupled wave analysis and compared to traditional thin film coatings. The design of the moth-eye arrays is optimized for application to a laboratory cell (air–silicon interface) and an encapsulated cell (EVA-silicon interface), and the optimization accounts for the solar spectrum incident on the silicon interface in both cells, and the spectral response of both types of cell. The optimized moth-eye designs are predicted to outperform an optimized double layer thin film coating by approximately 2% for the laboratory cell and approximately 3% for the encapsulated cell. The predicted performance of the silicon moth-eye under encapsulation is particularly remarkable as it exhibits losses of only 0.6% compared to an ideal AR surfac

An Experimental Evaluation of Nearest Neighbour Time Series Classification

Data mining research into time series classification (TSC) has focussed on alternative distance measures for nearest neighbour classifiers. It is standard practice to use 1-NN with Euclidean or dynamic time warping (DTW) distance as a straw man for comparison. As part of a wider investigation into elastic distance measures for TSC~\cite{lines14elastic}, we perform a series of experiments to test whether this standard practice is valid. Specifically, we compare 1-NN classifiers with Euclidean and DTW distance to standard classifiers, examine whether the performance of 1-NN Euclidean approaches that of 1-NN DTW as the number of cases increases, assess whether there is any benefit of setting $k$ for $k$-NN through cross validation whether it is worth setting the warping path for DTW through cross validation and finally is it better to use a window or weighting for DTW. Based on experiments on 77 problems, we conclude that 1-NN with Euclidean distance is fairly easy to beat but 1-NN with DTW is not, if window size is set through cross validation

Predictive Modelling of Bone Age through Classification and Regression of Bone Shapes

Bone age assessment is a task performed daily in hospitals worldwide. This involves a clinician estimating the age of a patient from a radiograph of the non-dominant hand. Our approach to automated bone age assessment is to modularise the algorithm into the following three stages: segment and verify hand outline; segment and verify bones; use the bone outlines to construct models of age. In this paper we address the final question: given outlines of bones, can we learn how to predict the bone age of the patient? We examine two alternative approaches. Firstly, we attempt to train classifiers on individual bones to predict the bone stage categories commonly used in bone ageing. Secondly, we construct regression models to directly predict patient age. We demonstrate that models built on summary features of the bone outline perform better than those built using the one dimensional representation of the outline, and also do at least as well as other automated systems. We show that models constructed on just three bones are as accurate at predicting age as expert human assessors using the standard technique. We also demonstrate the utility of the model by quantifying the importance of ethnicity and sex on age development. Our conclusion is that the feature based system of separating the image processing from the age modelling is the best approach for automated bone ageing, since it offers flexibility and transparency and produces accurate estimate

Читая папирусы. Создавая древнюю историю

Автор демонстрирует методы сбора исторических данных из разрозненных и нередко плохо сохранившихся источников для воссоздания картины общественной, экономической и культурной жизни многоязычного и многонационального античного мира. В процессе анализа современных методов работы с древними текстами он также исследует альтернативные пути освоения этих документов.Настоящая монография издана в серии "Approaching the Ancient World". Приложения: список цитируемых современных работ (с. 130-136), тематическая библиография (с. 137-141), общий указатель (с. 142-145)

Photovoltaic technologies

Photovoltaics is already a billion dollar industry. It is experiencing rapid growth as concerns over fuel supplies and carbon emissions mean that governments and individuals are increasingly prepared to ignore its current high costs. It will become truly mainstream when its costs are comparable to other energy sources. At the moment, it is around four times too expensive for competitive commercial production. Three generations of photovoltaics have been envisaged that will take solar power into the mainstream. Currently, photovoltaic production is 90% first-generation and is based on silicon wafers. These devices are reliable and durable, but half of the cost is the silicon wafer and efficiencies are limited to around 20%. A second generation of solar cells would use cheap semiconductor thin films deposited on low-cost substrates to produce devices of slightly lower efficiency. A number of thin-film device technologies account for around 5–6% of the current market. As second-generation technology reduces the cost of active material, the substrate will eventually be the cost limit and higher efficiency will be needed to maintain the cost-reduction trend. Third-generation devices will use new technologies to produce high-efficiency devices. Advances in nanotechnology, photonics, optical metamaterials, plasmonics and semiconducting polymer sciences offer the prospect of cost-competitive photovoltaics. It is reasonable to expect that cost reductions, a move to second-generation technologies and the implementation of new technologies and third-generation concepts can lead to fully cost- competitive solar energy in 10–15 years

Health Trainers End of Year Review 1st April 2013 – 31st March 2014

A critical assessment of health trainer activity, with particular reference to the most deprived social groups, and a focus on mental health and wellbeing

Systematic review of transition models for young people with long-term conditions: A report for NHS Diabetes.

Aims For many young people with Type 1 diabetes, transition from paediatric to adult care can result in a marked deterioration in glycaemic control. A systematic review assessed the effectiveness of transition models, or components of models, for managing the transition process in young people with long-term conditions, including Type 1 diabetes. This involved identifying (i) the main barriers and facilitators in implementing a successful transition programme, and (ii) the key issues for young people with long-term conditions and professionals involved in the transition process. Methods The following databases were searched from inception to August 2012: MEDLINE, EMBASE, PsychINFO, CINAHL, ASSIA, Social Services Abstracts, Academic Search Complete, Social Science Citation Index, Cochrane and Campbell Libraries. Selected studies included young people aged 11 to 25 diagnosed with long-term conditions who were in transition from paediatric to adult secondary health care services. Results 16 systematic reviews and 13 primary studies were included from 9992 records retrieved. No single transition model was uniquely effective. The most successful transitions centred around: young person-focused; age and developmentally appropriate content and delivery; self-management education; family participation; paediatric and adult collaboration; designated transition clinics; transition co-ordinator; young person’s portfolio; specific professionals training; multidisciplinary approach; structured process embedded in service delivery. There were no distinctive characteristics of condition-specific Type 1 diabetes services. Conclusion This important and timely review summarises the key factors that need to be considered for the development of transition programmes for young people with long-term conditions, including those with Type 1 diabetes

Finding Motif Sets in Time Series

Time-series motifs are representative subsequences that occur frequently in a time series; a motif set is the set of subsequences deemed to be instances of a given motif. We focus on finding motif sets. Our motivation is to detect motif sets in household electricity-usage profiles, representing repeated patterns of household usage. We propose three algorithms for finding motif sets. Two are greedy algorithms based on pairwise comparison, and the third uses a heuristic measure of set quality to find the motif set directly. We compare these algorithms on simulated datasets and on electricity-usage data. We show that Scan MK, the simplest way of using the best-matching pair to find motif sets, is less accurate on our synthetic data than Set Finder and Cluster MK, although the latter is very sensitive to parameter settings. We qualitatively analyse the outputs for the electricity-usage data and demonstrate that both Scan MK and Set Finder can discover useful motif sets in such data